Biometric system provides more reliable and efficient means of identity verification. Gait recognition is the process of identifying a person by the way they walk. It is one kind of biometric technology that can be used to monitor people without their co-operation and has been receiving wide attention in the computer vision community. In this paper, we propose a new approach for extracting human gait features from a walking subject based on wavelet coefficients and geometrical features of the silhouette. The proposed system is tested on CASIA dataset. The experimentation results indicate that the proposed system works efficiently by combining geometrical features and wavelet coefficients.The proposed decision fusion enables the performance improvement by integrating multiple ones with different confidence measures.
In the current era, the majority of public places such as supermarket, public garden, malls, university campus, etc. are under video surveillance. There is a need to provide essential security and monitor unusual anomaly activities at such places. The major drawback in the traditional approach, that there is a need to perform manual operation for 24 ? 7 and also there are possibilities of human errors. This paper focuses on anomaly detection and activity recognition of humans in the videos. Computer vision has evolved in the last decade as a key technology for numerous applications replacing human supervision. We present an e?cient method for detecting anomalies in videos. Recent applications of convolutional neural networks have shown promises of convolutional layers for object detection and recognition, especially in images. Experimental results on challenging datasets show the superiority of the proposed method compared to the state of the art in both frame-level and pixel-level in anomaly detection task.
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